Interpretive Summary: Plant simulation models and decision support systems are continually being developed and applied to various agricultural problems such as global climate change. Plant parameters are critical inputs required to run these models, and make a general set of algorithms applicable to specific cultivars. A subset of plant parameters are often referred to as Agenetic coefficients.@ However, these genetic coefficients are developed from phenotypic observations (i.e., what the plant looks like in the field), usually have a weak genetic basis, and are at best Agenotypic@ coefficients because they consider the genotype (i.e., the entire set of genes in the plant) as a whole. With our increased understanding derived from mapping crop genomes, we believe models can be improved by directly relating specific genes to develop more realistic genetic coefficients and better portray our knowledge of the linkage between gene function and plant phenotype in simulation models. As an example, we discuss how knowledge of height genes in wheat (Triticum aestivum L.) cultivars, along with stronger genetic and environmental response algorithms, could substitute for the phenotypic parameter Aheight class@ in the crop simulation model SHOOTGRO to provide a genetic basis for the model parameters. We also demonstrate how models containing responses based on known genetic variation can be used to identify traits to incorporate into cultivars better adapted to future climate scenarios. It remains for the geneticist, plant breeder, physiologist, and modeler to cooperate and communicate with each other so that genetic information and responses with the genotype and environment and their interaction can be described in models and used to develop cultivars better able to exploit future climatic conditions.

Technical Abstract:
Plant parameters are critical inputs in crop simulation models, and make a general set of algorithms applicable to specific cultivars. A subset of plant parameters are often referred to as Agenetic coefficients.@ However, these genetic coefficients are developed from phenotypic observations, usually have a weak genetic basis, and are at best Agenotypic@ coefficients because they consider the genotype as a whole and likely include some impact of environment on the trait or characteristic described. With our increased understanding of crop genomes, we believe models can be improved by directly relating specific genes (taking into account epistasis) to develop more realistic genetic coefficients and better portray our knowledge of the linkage between gene function and plant phenotype in simulation models. As an example, we discuss how knowledge of height genes in wheat (Triticum aestivum L.) cultivars, along with stronger genetic and environmental response algorithms, could substitute for the phenotypic parameter Aheight class@ in SHOOTGRO to provide a genetic basis for the model parameters. We also demonstrate how models containing responses based on known genetic variation can be used to identify traits to incorporate into cultivars better adapted to future climate scenarios. It remains for the geneticist, plant breeder, physiologist, and modeler to cooperate and communicate with each other so that genetic information and responses with the genotype and environment and their interaction can be described in models and used to develop cultivars better able to exploit future climatic conditions.